9 papers
AdaPS-LiNGAM: Adaptive Predecessor Selection for Linear Non-Gaussian Acyclic Models under Small-Sample Settings
Shun Yanashima, Kentaro Kanamori, Hirofumi Suzuki
Causal discovery becomes particularly challenging when the available sample size is small relative to the number of variables. This challenge also arises in the linear non-Gaussian…
I-CAM-UV: Integrating Causal Graphs over Non-Identical Variable Sets Using Causal Additive Models with Unobserved Variables
Hirofumi Suzuki, Kentaro Kanamori, Takuya Takagi +3
Causal discovery from observational data is a fundamental tool in various fields of science. While existing approaches are typically designed for a single dataset, we often need to…
Sparse Additive Model Pruning for Order-Based Causal Structure Learning
Kentaro Kanamori, Hirofumi Suzuki, Takuya Takagi
Causal structure learning, also known as causal discovery, aims to estimate causal relationships between variables as a form of a causal directed acyclic graph (DAG) from observati…
Learning Decision Trees and Forests with Algorithmic Recourse
Kentaro Kanamori, Takuya Takagi, Ken Kobayashi +1
This paper proposes a new algorithm for learning accurate tree-based models while ensuring the existence of recourse actions. Algorithmic Recourse (AR) aims to provide a recourse a…
Learning Locally Interpretable Rule Ensemble
Kentaro Kanamori
This paper proposes a new framework for learning a rule ensemble model that is both accurate and interpretable. A rule ensemble is an interpretable model based on the linear combin…
Algorithmic Recourse with Missing Values
Kentaro Kanamori, Takuya Takagi, Ken Kobayashi +1
This paper proposes a new framework of algorithmic recourse (AR) that works even in the presence of missing values. AR aims to provide a recourse action for altering the undesired…
Computing the Collection of Good Models for Rule Lists
Kota Mata, Kentaro Kanamori, Hiroki Arimura
Since the seminal paper by Breiman in 2001, who pointed out a potential harm of prediction multiplicities from the view of explainable AI, global analysis of a collection of all go…
BRPO: Batch Residual Policy Optimization
Sungryull Sohn, Yinlam Chow, Jayden Ooi +4
In batch reinforcement learning (RL), one often constrains a learned policy to be close to the behavior (data-generating) policy, e.g., by constraining the learned action distribut…
Enumeration of Distinct Support Vectors for Interactive Decision Making
Kentaro Kanamori, Satoshi Hara, Masakazu Ishihata +1
In conventional prediction tasks, a machine learning algorithm outputs a single best model that globally optimizes its objective function, which typically is accuracy. Therefore, u…